Data & Analytics
One source of truth across every tool your team uses.
We build the data warehouse, ETL pipelines, and dashboards that unify your fragmented systems — so finance closes month-end in hours, not days, and every report answers the question it was meant to.
Tell us what's disconnectedWhen data & analytics makes sense
These are the situations businesses describe when they first reach out.
Reports are built by manually copying and pasting from four or five different systems each week.
Your CRM, payment processor, e-commerce platform, and accounting software don't talk to each other.
Finance closes month-end in spreadsheets that take two days to reconcile.
Your team argues about which number is correct — because different tools show different figures.
You're a nonprofit or NGO that needs donor analytics but can't afford an enterprise data platform.
What we build
Data warehouse architecture
Schema design with staging, transformation, and mart layers that scale as your data grows. We choose the right warehouse — BigQuery, Snowflake, Redshift, or DuckDB — based on your volume, query patterns, and budget.
ETL & ELT pipeline build
Extract from your source systems, transform to your business logic, load into your warehouse. Pipelines run on a schedule or trigger-based. Full monitoring, alerting, and automatic retry on failure.
Dashboard & reporting layer
Looker Studio, Metabase, or custom React dashboards giving stakeholders the numbers they need — without having to ask the data team. Role-based access so everyone sees what they should.
Source system integrations
Salesforce, HubSpot, Stripe, PayPal, Shopify, WooCommerce, QuickBooks, Xero, Givecloud, and more. We've integrated most of the SaaS tools SMEs and nonprofits use — and can build custom extractors for tools without native connectors.
Nonprofit & donor analytics
Donation tracking, donor retention cohorts, grant reporting, and program impact dashboards built specifically for NGOs and foundations. Designed for program teams, not data engineers.
Data quality & governance
Schema validation, null checks, anomaly detection, and lineage tracking. You know when the pipeline breaks before your CFO notices a wrong number in their report.
Our stack
dbt (data build tool) for transformation, Airbyte for ELT ingestion, Apache Airflow or Cloud Composer for orchestration, BigQuery or Snowflake as the primary warehouse, Looker Studio and Metabase for dashboards, Python (pandas, SQLAlchemy) for custom extractors, and PostgreSQL/Supabase for operational databases.
Case study
Unified donor data warehouse for Blue Dragon Children's Foundation
Blue Dragon Children's Foundation — one of Vietnam's most recognised rescue and rehabilitation NGOs — was operating with donor data spread across Salesforce, Givecloud, BigQuery, and PayPal. Finance and program teams each maintained separate spreadsheets, reconciling them manually at month-end. Reports for major donors and grant applications took two days to prepare and still contained discrepancies. L'inno built a unified data warehouse on BigQuery, ingesting from all four source systems via Airbyte. dbt transformation layers cleaned and unified donor records, calculated retention cohorts, and surfaced program impact metrics. The finance team now closes monthly donor reports in under three hours. The program team accesses live dashboards without waiting for a data export. Grant reporting that used to take two days now takes one morning — with confidence in every figure.
Read the full case studyHow we work
Data audit (free, 2–3 hours)
We review your source systems, current reporting process, biggest data frustrations, and the decisions you most need better data for.
Architecture design & fixed-price proposal (1 week)
Warehouse schema, ETL plan, dashboard wireframes, and a fixed quote. You see the architecture before we write a line of code.
Build in iterations (4–8 weeks)
Warehouse, pipelines, and dashboards built in weekly increments. You see working data in staging each week — not a big-bang launch.
Handover, documentation & training
Full documentation of the warehouse schema, pipeline logic, and dashboard definitions. Your team can maintain and extend the system without us.
Frequently asked questions
Do we need a data team to maintain this after you build it?
No. We design the warehouse and pipelines to be maintainable by non-specialists. Dashboards are self-serve. Pipeline failures trigger alerts so problems surface proactively. If your data needs grow significantly, we can discuss a lightweight retainer — but most clients maintain the system themselves after handover.
How long until we have our first working dashboard?
For a typical 2–3 source integration, you'll see data in staging within 2–3 weeks of project start. A production dashboard follows within 4–6 weeks total. The first iteration focuses on the highest-value metrics; additional reports are added iteratively.
Can you work with our existing spreadsheets as a data source?
Yes — Google Sheets and Excel files can be connected as data sources. We typically treat spreadsheets as a transitional source and help you move upstream to cleaner inputs over time, but they work as a starting point.
What's the difference between a data warehouse and a dashboard tool like Google Data Studio?
Looker Studio, Power BI, and similar tools are visualization layers — they display data but don't clean, unify, or model it. A data warehouse is where the logic lives: how metrics are defined, how sources are reconciled, how history is preserved. Without a warehouse, dashboards built on raw source data show whatever noise is in the source. With a warehouse, every report draws from the same clean, agreed-upon definitions.
How do you handle sensitive data like donor personal information or financial records?
We implement role-based access control from day one: each dashboard role sees only the data appropriate to it. PII is handled according to your data policy — we can pseudonymize, tokenize, or restrict access at the column level. For nonprofits subject to donor privacy commitments, we design the warehouse schema to enforce those boundaries technically, not just by policy.